Chemical plant protection is an essential element in integrated pest management and hence, in current crop production. The use of Plant Protection Products (PPPs) potentially involves ecological risk. This risk has to be characterised, assessed and managed.
For the coming years, an increasing need for agricultural products is expected. At the same time, preserving our natural resources and biodiversity per se is of equally fundamental importance. The relationship of our economic success and cultural progress to protecting the environment has been made plain in the Ecosystem Service concept. These distinct 'services' provide the foundation for defining ecological protection goals (Specific Protection Goals, SPGs) which can serve in the development of methods for ecological risk characterisation, assessment and management.
Ecological risk management (RM) of PPPs is a comprehensive process that includes different aspects and levels. RM is an implicit part of tiered risk assessment (RA) schemes and scenarios, yet RM also explicitly occurs as risk mitigation measures. At higher decision levels, RM takes further risks, besides ecological risk, into account (e.g., economic). Therefore, ecological risk characterisation can include RM (mitigation measures) and can be part of higher level RM decision-making in a broader Ecosystem Service context.
The aim of this thesis is to contribute to improved quantification of ecological risk as a basis for RA and RM. The initial general objective had been entitled as "… to estimate the spatial and temporal extent of exposure and effects…" and was found to be closely related to forthcoming SPGs with their defined 'Risk Dimension'.
An initial exploration of the regulatory framework of ecological RA and RM of PPPs and their use, carried out in the present thesis, emphasised the value of risk characterisation at landscape-scale. The landscape-scale provides the necessary and sufficient context, including abiotic and biotic processes, their interaction at different scales, as well as human activities. In particular, spatially (and temporally) explicit landscape-scale risk characterisation and RA can provide a direct basis for PPP-specific or generic RM. From the general need for tiered landscape-scale context in risk characterisation, specific requirements relevant to a landscape-scale model were developed in the present thesis, guided by the key objective of improved ecological risk quantification. In principle, for an adverse effect (Impact) to happen requires a sensitive species and life stage to co-occur with a significant exposure extent in space and time. Therefore, the quantification of the Probability of an Impact occurring is the basic requirement of the model. In a landscape-scale context, this means assessing the spatiotemporal distribution of species sensitivity and their potential exposure to the chemical.
The core functionality of the model should reflect the main problem structures in ecological risk characterisation, RA and RM, with particular relationship to SPGs, while being adaptable to specific RA problems. This resulted in the development of a modelling framework (Xplicit-Framework), realised in the present thesis. The Xplicit-Framework provides the core functionality for spatiotemporally explicit and probabilistic risk characterisation, together with interfaces to external models and services which are linked to the framework using specific adaptors (Associated-Models, e.g., exposure, eFate and effect models, or geodata services). From the Xplicit-Framework, and using Associated-Models, specific models are derived, adapted to RA problems (Xplicit-Models).
Xplicit-Models are capable of propagating variability (and uncertainty) of real-world agricultural and environmental conditions to exposure and effects using Monte Carlo methods and, hence, to introduce landscape-scale context to risk characterisation. Scale-dependencies play a key role in landscape-scale processes and were taken into account, e.g., in defining and sampling Probability Density Functions (PDFs). Likewise, evaluation of model outcome for risk characterisation is done at ecologically meaningful scales.
Xplicit-Models can be designed to explicitly address risk dimensions of SPGs. Their definition depends on the RA problem and tier. Thus, the Xplicit approach allows for stepwise introduction of landscape-scale context (factors and processes), e.g., starting at the definitions of current standard RA (lower-tier) levels by centring on a specific PPP use, while introducing real-world landscape factors driving risk. With its generic and modular design, the Xplicit-Framework can also be employed by taking an ecological entity-centric perspective. As the predictive power of landscape-scale risk characterisation increases, it is possible that Xplicit-Models become part of an explicit Ecosystem Services-oriented RM (e.g., cost/benefit level).